Physical Sciences and Mathematics Commons^™

Leveraging Quantum Storage Mechanism For Digital Forensic Readiness Towards Smart City Security, Bashaer Aljeneibi, Richard Ikuesan

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The increasing digitization of urban infrastructure has introduced advanced efficiency and connectivity in smart cities while exposing them to sophisticated cybersecurity threats. This study explores how Quantum Storage Mechanisms (QSM) can be integrated with digital forensic readiness systems to enhance smart city security and incident response. Through a simulated environment, the research evaluates the effectiveness of QSM against three critical cyberattack scenarios: Distributed Denial of Service (DDoS), sensor spoofing, and supply chain firmware attacks. The findings reveal that QSM-enabled systems outperform traditional cybersecurity tools by ensuring tamper-proof evidence collection, real-time threat detection, and secure long-term data retention. The study also …

Data Repository For The Distribution Of Small Impact Craters Along The Msl Curiosity Rover Traverse In Gale Crater, Mars, Megan E. Hoffman

Earth and Planetary Sciences Faculty and Staff Publications

The theoretical limit of the smallest impact crater diameter that can form under current martian atmospheric conditions is approximately 0.25 m, which is near the projected pixel size of the best orbital imagers around Mars. As a result, there have been limited studies into how the retention of the smallest impact craters on Mars varies with surface properties, since orbital images alone cannot confidently resolve them. Rovers and landers are needed to confidently differentiate and measure these smallest craters. Here we catalog small impact craters seen along the first 25 km, or 3120 sols of the Mars Curiosity rover traverse …

Optimized Hybrid Beamforming For Ris-Assisted Multi-User Mimo In 6g Mmwave Networks: A Low-Complexity Approach To Spectral Efficiency And Interference Mitigation, Safiya Nasser Al-Jaradi, Mohammad Kamrul Hasan, Nabeel Al-Qirim, Shayla Islam, Thippa Reddy Gadekallu, Hana Mujlid, Hashim Elshafie, Rashid A. Saeed

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The joint optimization of hybrid beamforming and reconfigurable intelligent surface (RIS) phase shifts in multi-user millimeter-wave (mmWave) MIMO systems is a challenging problem, mainly due to high computational complexity and the lack of adaptive interference management. Existing approaches typically rely on fixed Zero-Forcing (ZF) or Maximum-Ratio Transmission (MRT) designs or require iterative optimization with high overhead, limiting their practical use in dense 6G environments. To overcome these challenges, this research proposes a RIS-Aided Adaptive Zero-Forcing and Maximum-Ratio Transmission Hybrid Precoding (RA-ZMHP) framework for 6G mmWave multi-user MIMO systems. The main novelty of the method lies in an adaptive ZF–MRT mixing …

Rumooz Aljareemah: An Intelligent Search System For Uae Criminal Law With Case Correlation Framework For Forensic Investigators, Rahaf Alnuaimi, Maryam Almarzooqi

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Digital forensic investigations in the UAE encounter dual challenges: effectively correlating data across cases and complying with local legal frameworks. Conventional methods create information silos that hide connections between instances and increase the probability of procedural errors. This paper presents RUMOOZ ALJAREEMAH, a prototype platform for case correlation designed for cybersecurity experts and forensic investigators in the UAE. The approach integrates a correlation engine with a UAE-specific legal compliance framework, using authentication protocols, bilingual assistance, and text-based search algorithms. Our theoretical framework suggests enhancements in investigative efficiency, including reduced case resolution durations, improved identification of cross-case relationships, and a decrease …

Blockchain-Based Nostrification: A Privacy-Preserving Framework For Documents Verification, Mohamad Badra, Rouba Borghol

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Many employers and institutions will not complete a hire until they verify a candidate's foreign qualifications. This nostrification step exists for a simple reason: they need to know that certificates, medical records, financial papers, and other official documents are real and not forged. For years, signatures and stamps were enough. But the shift to online applications changed the game. Today, anyone can upload a polished PDF, and with basic editing tools, fake documents can be created in minutes. The old system no longer protects anyone. On the other hand, the blockchain offers a stronger and more practical solution. Instead of …

Cliffinsight: An Educational Web Application That Visualizes The Calculation Of Effect-Sizes Using Cliff's Delta, Mohamed El-Attar, Ahmed Shuhaiber, Rima Grati, Sarah Kohail

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The purpose of calculating effect sizes in statistics is to quantify the practical significance of observed differences beyond mere statistical significance. While standardized mean difference measures such as Cohen’s d are widely used, they require normally distributed data, an assumption frequently violated in educational and social science research. Non-parametric alternatives such as Cliff’s delta (δ) are more robust under these conditions yet remain underused due to perceived computational complexity and limited accessible resources. Existing web-based tools for Cliff’s delta function primarily as numerical calculators and do not expose the underlying dominance structure that gives the statistic its meaning. This paper …

Challenges In Engineering Machine Learning (Software) Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides on the software engineering challenges unique to machine learning systems, for an undergraduate software engineering course. After contrasting traditional programming with machine learning, the deck examines why the usual tools for managing complexity—abstraction, reuse, and composition—are harder to apply to ML, given the lack of clear specifications and modularity. It covers concept drift, feedback loops (illustrated with crime-prediction and recommendation examples), and the accumulation of technical debt in ML systems, including the role and pitfalls of notebooks in moving from experimentation to production. Based on "Machine Learning in Production/AI Engineering" by Christian Kaestner and Eunsuk Kang (Carnegie Mellon …

Data For "Large Projected Increases In Area Burned And Wildfire Frequency By 2050 In Utah, Usa", Joseph D. Birch, Yoshimitsu Chikamoto, James A. Lutz

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Changes in wildfire regimes may disrupt ecosystem processes as wildfires burn larger areas or burn more frequently than the recent natural range of variability. The climatic drivers of wildfire behavior may change in strength but these effects are not likely to be uniform across space and between different vegetation types. Increased understanding of how weather and climate influence patterns of burn area and frequency across vegetation types may assist in better predicting and managing future wildfire regimes. We examined a dataset of all 1469 wildfires ≥40 ha from 1984 – 2021 in Utah, USA and used antecedent daily weather data …

Predicting Skin Concern Severity From Genetic And Lifestyle Factors: A Comparative Multi-Output Machine Learning Framework, Yassine Benachour, Lina Maloukh, Sadok Bouamama, Barbara Geusens

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Personalized dermatology increasingly leverages both genetic predispositions and lifestyle behaviors to model individual skin health outcomes. This study proposes a multi-output machine learning framework to predict the severity of six dermatological phenotypes—acne, redness, dryness, sensitivity, scarring, and pigmentation—using a multimodal dataset of 5,254 individuals. Input features include mutation profiles for six skin-related genes (FLG, MMP1, MMP3, AQP3, SOD2, GPX) and 22 lifestyle variables such as sun exposure, stress, and hydration. We train and evaluate LightGBM models under independent, multi-output, and chained configurations. Performance is assessed using Mean Absolute Error (MAE) and average Quadratic Weighted Kappa (QWK). The proposed ordinal-aware independent …

Enhancing Breast Cancer Detection In Mammographic Imaging Using Explainable Clinical Decision Support System And Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

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Breast cancer remains one of the leading causes of mortality among women worldwide, where early and precise detection plays a vital role in improving survival rates and treatment outcomes. However, conventional deep learning approaches often encounter challenges in handling dense mammographic tissues and lack transparency in decision-making, limiting their clinical reliability. To address these limitations, this study introduces TransYOLO-GJO, an explainable and optimized detection framework that integrates transformer-based attention mechanisms into the YOLOv9 architecture and leverages the Golden Jackal Optimization (GJO) algorithm for hyperparameter tuning. The transformer encoder enhances contextual feature extraction, particularly in dense breast regions, while GJO dynamically …

Gets: Greenhouse Environment Time Series, Scott Grimshaw, Natalie J. Blades, Grant R. Mcqueen

ScholarsArchive Data

This dataset contains high-frequency environmental measurements from a single greenhouse used to study multivariate statistical process control under strong autocorrelation. The data consist of a continuous Phase 1 monitoring period of approximately four weeks, during which environmental sensors recorded conditions inside the greenhouse once per minute.

The primary variables included in the archived dataset are:

• date – Date-time stamp at one-minute resolution (local greenhouse time).

• co2_ppm – Carbon dioxide concentration in parts per million (ppm).

• humidity_pct – Relative humidity (%).

• soil_temp_F – Soil temperature in degrees Fahrenheit.

Efficient Routing For Software-Defined Wireless Sensor Networks: A Naïve Bayes Approach, Amine Tcherak, Samia Loucif, Mohamed Ould Khaoua

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Wireless Sensor Networks (WSNs) form the backbone of Internet of Things (IoT) applications. Software-Defined Networking (SDN) is an emerging networking paradigm that extends the lifetime of WSNs by transferring the resource-intensive routing task from sensor nodes to a centralized controller. However, many SDN-based routing schemes for WSNs employ inefficient algorithms at the controller. Traditional shortest-path methods often create traffic imbalances across neighboring nodes, while Reinforcement Learning (RL)-based approaches typically generate excessive control traffic. Both issues accelerate energy depletion and reduce network lifetime. Moreover, existing algorithms frequently overlook critical factors, such as buffer occupancy, when selecting relay nodes, which can lead …

Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari

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The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

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Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani

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Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh

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Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs …

Fall 2024 Computer Programming And Engineering Self-Efficacy Survey Data, Mary Benjamin

Michigan Tech Research Data

This dataset was collected as part of a research study examining the impact of automated code critiquers on students’ programming and engineering self-efficacy in first-year engineering courses. The study involved pre- and post-surveys administered to students enrolled in ENG1101: Introduction to Engineering during Fall 2024 at Michigan Technological University. The research aims to understand how exposure to automated feedback tools, such as WebTA, influences confidence, persistence, and perceived competencies.

Retaining The Herd: Habitat Restoration Efforts Increase Patch Retention Times In Mule Deer, Jack Rasmussen

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Across much of their range, and the State of Utah, mule deer (Odocoileus hemionus) populations have been declining. Declines in herd numbers can be attributed to alterations to historic habitats through the introduction of exotic annual grasses and the encroachment of pinyon-juniper forests on sagebrush ecosystems, changing resource availability. To combat habitat alterations, the Utah Department of Natural Resources has implemented various habitat restoration treatments through the Watershed Restoration Initiative , broadly to remove exotic species and decrease pinyon juniper woodland encroachment. We investigated how mule deer patch retention times differ on eight different habitat restoration treatment types during summer …

Data Supporting “High-Resolution Lidar Observations Of Sedimentation-Induced Size Sorting Of Droplets Near A Laboratory Cloud Top”, Fan Yang, Yong Meng Sua, Zipei Zheng, Jesse Anderson, Hamed F. Sadi, Jae Min Yeom, Suryadey P. Singh, Pei Hou, Will Cantrell, Ernie R. Lewis, Alexander Kostinski, Raymond Shaw

Michigan Tech Research Data

Cloud optical properties and precipitation, which are crucial to weather and climate, are strongly influenced by cloud microphysical properties that are still poorly understood. Here, we develop a high-resolution time-correlated single-photon-counting lidar and apply it to observe cloud microphysical properties at one-centimeter range resolution in a convection chamber under well-controlled conditions. Together with concurrent in-situ measurements and theoretical analysis, our lidar observations indicate that although turbulent mixing tends to homogenize the cloud in the bulk region, entrainment and sedimentation cause inhomogeneities in droplet concentrations near the cloud top. Specifically, the topmost region is directly affected by entrainment, and lidar profiles …

Culture Mediates Climate Opinion Change: A System Dynamics Model, Louis (Lou) Gross, Yoon Ah Shin, Sara M. Constantino, Ann Kinzig, Katherine Lacasse, Brian Beckage

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Novel Mathematical Model Of Oropouche Virus Transmission Dynamics, Darsh Gandhi, Carli Peterson, Emma Slack, Elizabeth Rubio, Amira Claxton, Christopher M. Kribs

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling Social Isolation: A Data-Driven Index Function Design And Implementation, Jeremis N. Morales Morales, Carmen Caiseda, Phyllis Muniu, Joshua Atsu, Folashade B. Agusto

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Metapopulation Model For Oyster Restoration, Leah Shaw

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

[Chu] Modelling The Effect Of Disturbed Blood-Feeding On Mosquito-Borne Disease Transmission, Kyle Dahlin, Michael Robert, Lauren Childs

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Energy Input-Output Meta-Analysis Reveals Algal Diesel Struggles To Break Even (Supplemental Information), Michelle Arnold

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1. Algal biofuels have been investigated as an alternative to traditional fossil fuels for transportation in the United States since the 1970s. Yet after five decades of development, scalability and implementation remain limited. To evaluate the state of progress, this study harmonized energy inputs and outputs across 39 papers, encompassing 508 observations on the production of algal biofuel energy return on energy investment (EROEI) in the United States. The analysis produced a mean EROEI of 1.01—essentially the break-even point—irrespective of system boundaries. This is lower than bioethanol (2.8) and far below oil (8.7). Life-cycle analysis results showed that hydrothermal liquefaction in …

Increasing Complexity Leads To Declining Productivity Of Innovation In The Algal Biofuel Industry (Supplemental Information), Michelle Arnold

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Liquid biofuels are widely promoted as renewable, lower-emission alternatives to fossil fuels. Yet despite their promise, this sector faces a critical constraint: a long-term decline in the productivity of innovation. This decline—measured by a decreasing number of patents per inventor—suggests that generating technological breakthroughs is becoming more difficult, expensive, and uncertain. As innovation becomes more resource-intensive, the feasibility of replacing petroleum with liquid biofuels grows more tenuous. This is especially problematic given the pre-existing technical disadvantages of biofuels, including their relatively low energy return on energy investment (EROEI) and energy density compared to fossil fuels. Our findings show that the …

Data Accompanying "Landscape Composition Surrounding Restoration Projects Modulates Use By Wildlife: A Case Study Of Mule Deer In Utah", Jaylin Solberg

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With mule deer (Odocoileus hemionus), hereafter, deer, populations in decline, wildlife managers have increased their efforts to improve factors that limit population growth. In the State of Utah (hereafter, Utah), these efforts have included habitat restoration which is expected to improve environmental conditions for deer. Some of these efforts include pinon-juniper removal, prescribed burning, and spraying of invasive plant species. Although previous work has demonstrated the local impacts of restoration on deer populations, it is unknown how the environmental conditions in the landscape surrounding restoration treatments may modify local restoration impacts. Identifying how both local and landscape-scale conditions influence deer …

A New Scheme For Simple Asymmetric Bivariate Copulas And Applications, Rachid Bentoumi, Farid El Ktaibi, Christophe Chesneau

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Bivariate copulas play a central role in modeling the dependence structure between two random variables and serve as a fundamental tool in various applied fields. In this article, we develop a new theoretical framework aimed at constructing simple asymmetric bivariate copulas of the form C(u, v) = uv [ϕ(v) + u(1 − ϕ(v))^], (u, v) ∈ [0, 1]². This framework relies on a tuning univariate function to achieve the desired asymmetry. We study this pioneering scheme, emphasizing its theoretical foundations, and illustrating it with several examples. More precisely, we establish important properties of the proposed copulas …

Efficient Smooth Tensor Train And Tensor Ring Completion For Image Classification Enhancement, Salman Ahmadi-Asl, Roman V. Garaev, Rustam A. Lukmanov, Naeim Rezaeian, Asad Masood Khattak, Manuel Mazzara

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This paper deals with studying the data completion problem for enhancing the image classification task under the pixel removal scenario. In some applications, it happens that a part of the pixels of a given image is lost due to several issues, such as corruption by outliers or artifacts and/or incompleteness due to imprecise data acquisition. This issue results in a completely wrong classification outcome using Deep Neural Networks (DNNs). In this paper we investigate the benefit of data completion in enhancing the classification accuracy of the DNN models to build more robust and stable DNN models. To this end, we …